An approach that utilizes blockchain to effectively and securely preserve data privacy for location data from IoT in smart cities

Abstract. Environmental surveillance, emergency response, and smart city planning all require the use of geospatial data, which includes satellite imagery, cartographic records, and real-time GPS coordinates. The high sensitivity and value of location-specific information make it unsafe to store and transmit it through conventional, centralized means, which can result in privacy breaches, unauthorized manipulations, and potential misuse. This paper aims to design and implement a secure, blockchain-based framework that blends AES (Advanced Encryption Standard) and RSA (Rivest–Shamir–Adleman) key management, which addresses these challenges. The aim is to guarantee strong data confidentiality by using symmetric encryption, and to use public-key cryptography for granular access control and secure key distribution. The proposed system uses Ethereum smart contracts to connect encrypted data references to a decentralized ledger, ensuring tamper resistance and auditability. In the proposed system, a Python-based FastAPI backend is responsible for data ingestion, cleaning, encryption, and blockchain interaction, while a React frontend can upload datasets, generate encryption keys, and retrieve access permissions. Modular microservices and well-defined APIs can seamlessly integrate various components, such as data processing scripts and on-chain contract logic, during development. The system's scalability is demonstrated by evaluating its performance against various dataset sizes, which involves metrics such as encryption overhead, blockchain transaction costs, and smart contract execution times. The practical usability of the system in actual scenarios is demonstrated through user acceptance testing, which is crucial for adoption in resource-limited environments. The results show the proposed crypto-enhanced blockchain framework can significantly enhance geospatial data security while still maintaining operational efficiency. Integration with zero-knowledge proofs may be explored in future work to enhance privacy, mitigate energy costs through alternative consensus algorithms, and enhance resilience in multi-network ecosystems through cross-chain interoperability.